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Genets

Genets are a group of small, nocturnal carnivores native to Africa and Asia.
These agile, tree-climbing mammals are known for their distinctive black-and-white or gray-and-white spotted coats.
Genets belong to the Viverridae family and are closely related to mongooses.
They are skilled hunters, feeding on small rodents, birds, insects, and even fruit.
Genets are solitary animals, using their sharp claws and prehensile tails to navigate the treetops.
Reseachers studying genets may be interested in their unique adaptations, behaviors, and ecological roles.
Pubcompare.ai's AI-powered platform can help locate the most reproducible and accurate research protocols related to genets, taking the guesswork out of your work.

Most cited protocols related to «Genets»

Carnivore Phylogenetic Protocol Database

The foundation of this data set lies in the 274 source trees from Bininda-Emonds et al. [1 (link)], which essentially covered the time period from 1970 to 1995 (with some pre-1970 publications). All source publications were re-reviewed and the source trees electronically saved exactly as they appeared in the original publication in NEXUS format [96 (link)] using Mesquite v2.x [97 ]. (In the original analysis, the MRP matrices were entered directly by hand into the data editor of MacClade v3.x [98 ].)
This data set was then expanded to cover papers published since 1995 (to August 2008) or those that were missed in the original set of analyses. Literature searches used diverse online databases-Carnivore Ecology & Conservation, Google Scholar, ScienceDirect, Scopus, the online catalog of the University of Jena/Provincial Library of Thuringia, Web of Science, Wiley InterScience and Zoological Record-using the search terms phylogen* or taxonom* or systemat* or cladistic* or clado* or classif* or morpholo* or crani* or bone or character or structu* in combination with the scientific or common names of each carnivore family. Secondary searches excluded publications including the keywords DNA or parasit* or molecul* or prädato* or genet* or mitoch*.
To counteract problems associated with data duplication among source trees and source trees of poor quality (see [29 (link)]), all source trees were subjected to the selection protocol outlined in Bininda-Emonds et al. [99 ] and excluded for one or more of the following reasons:
1. A publication date before 1970 so as to favor newer source trees based on more comprehensive data sets and analyzed using robust phylogenetic algorithms.
2. Insufficient information in the paper as to the data source underlying the source trees (for example, [100 (link)-105 (link)]).
3. Trees where characters were merely mapped onto an existing phylogeny unless it was explicitly mentioned that the characters were entirely congruent with the phylogeny (thereby representing independent support for it).
4. Papers lacking trees entirely and where the text was insufficient to accurately reconstruct the pattern of relationships implied for a source tree.
5. All molecular trees based on DNA sequence data that we were able to obtain separately (see below).
Where a publication contained more than one source tree, all independent source trees were identified using the protocols outlined in [99 ,106 (link)]. For non-independent source trees in the same publication, the preferred tree was the consensus of these trees in the first instance, followed by the most taxonomically complete tree or that explicitly preferred and justified by the authors. If none of these options were available, all source trees were coded and included in the main analysis; no mini-supertrees were made (contra [99 ]).
A total of 114 trees were thus obtained, 86.3% of which were also used by Bininda-Emonds et al. [1 (link)]. (The much smaller number compared with the original study (274) is because the original, nested supertree analysis meant that the same source tree could simultaneously contribute to more than one supertree analysis.) Of the original source trees, 27 were excluded for the reasons outlined above.
As a final step, the taxonomy of the source trees was standardized to the list of species names found in Wozencraft [14 ] using the Perl script synonoTree.pl v2.2 [99 ]. Taxon names that did not belong to crown group Carnivora were deleted outright, although the source trees containing them were held to be rooted (see below). For higher-level taxon names, the name-bearing type species was substituted wherever possible (for example, Canis lupus for Canidae); otherwise, the taxon was deleted from the source tree as was also the case for ambiguous names (for example, dog). synonoTree also accounts for species that occur more than once on a given tree through the synonymization process by outputting all possible permutations of the tree with the taxa represented once in all possible positions and combinations; these non-independent trees could later be down-weighted appropriately. Including permutations that arose because of the synonymization process, a total of 241 literature trees were obtained.

Nyakatura K, & Bininda-Emonds O.R. (2012). Updating the evolutionary history of Carnivora (Mammalia): a new species-level supertree complete with divergence time estimates. BMC Biology, 10, 12.

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Publication 2012

ARID1A protein, human Bones Canidae Carnivora Character Gap Junctions Genets Prosopis Trees Wolves

Arabidopsis Knockout Mutant Characterization

Arabidopsis thaliana ecotype Columbia was used for all experiments. For details of knockout mutants, see Table 1.

10.7554/eLife.03115.018

Table 1.

Details of knockout mutants

DOI:http://dx.doi.org/10.7554/eLife.03115.018

Gene number	Accession	Mutant number	Mutant name	Genotyping primer sequence	References
AT4G20140	Col-0	SALK_043282	sgn3-3	LP: ATTCTACGAGCCTTCCCATTC RP: CGCAGTGAACACAGTGAGATC	Present work
AT4G20140	Col-0	SALK_064029	sgn3-4 or gso1-1	LP: CTCGGCTCCCTCGTTAATATC RP: GTTACCTAAACTGGCGGGAAG	Tsuwamoto et al. (2008) (link) The Plant Journal
AT4G20140	Col-0	SALK_103965	sgn3-19	LP: TCCATTATGTGGTTCGAGCTC RP: CTTGTAAACCTTCCCAGAGCC	Present work
AT2G28670	Col-0	n.a.	esb1-1	n.a.	Baxter I et al. (2009) (link) PLOS Genet, Lahner B et al. (2003) (link) Nat Biotechnol
AT2G36100	Col-0	SAIL_265_H05	casp1-1	LP: GCGTTTCAGTACGTCCCTTC RP: CACGTGAGGGAAGTGAGTCTC	Roppolo et al. (2011) (link) Nature
AT2G27370	Col-0	SALK_011092	casp3-1	LP: GACTCTTCCTTTCTTCACTC RP: GACCAACACAACCGTACGAAC	Roppolo et al. (2011) (link) Nature

Pfister A., Barberon M., Alassimone J., Kalmbach L., Lee Y., Vermeer J.E., Yamazaki M., Li G., Maurel C., Takano J., Kamiya T., Salt D.E., Roppolo D, & Geldner N. (2014). A receptor-like kinase mutant with absent endodermal diffusion barrier displays selective nutrient homeostasis defects. eLife, 3, e03115.

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Publication 2014

Arabidopsis thalianas Ecotype Genets Oligonucleotide Primers Plants

Integrative Multi-Omics Analysis of Differentially Expressed Proteins

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Publication 2020

Genetic Processes Genets Proteins Psychological Inhibition

Genotyping Protocols for Pancreatitis

PRSS1 genotyping was done by DNA sequencing [69] (link). SPINK1 genotyping was done by sequencing exons 2–3 and the flanking regions in a preliminary subset of 745 NAPS2 cases, with the entire cohort (cases and controls) genotyped for p.N34S, p.P55S and c.27delC using TaqMan assays. The SPINK1 c.194+5G>A variant [70] (link) was seen in one patient and one control; c.194+2T>C [71] (link) was not identified in the initial sequencing and was not further genotyped.
CFTR variants for the screening panel were selected from a review of published papers and abstracts between 1998 and 2010 [16] (link), [17] (link), [43] –[52] (link) and the open access CFTR mutation database based in the Hospital for Sick Children in Toronto (http://www.genet.sickkids.on.ca) and Johns Hopkins University (Http://CFTR2.org).
CFTR genotyping was done using a custom MassARRAY iPLEX Gold assay (Sequenom, Inc, San Diego, CA) or custom TaqMan Gene Expression Assays (Life Technologies Corporation, Carlsbad, CA) through the Genomic and Proteomic Core Laboratories at the University of Pittsburgh and verified by bidirectional DNA sequencing. All cases and controls were tested for each of the 81 selected CFTR variants (Table S1). Variants were selected in three stages: the most common CF-causing mutations in North America, variations that have been reported in pancreatitis literature at least once and a subset of variants that have been reported in CF patients but for which the biological and pathological relevance remains undetermined (Mutations of Undetermined Clinical Significance). 67 SNPs (125GtoC, 1716G>A, 1717-1G>A, 1898+1G>A, 2183AA>G, 2184delA, 2789+5G>A, 3120+1G>A, 3659delC, 3849+10kbC>T, 621+1G>T, 711+5G>A, A455E, D110H, D1152H, D1270N, D443Y, D579G, F1052V, F1074L, F508C, F508del, G1069R, G1244E, G1349D, G178R, G542X, G551D, G551S, I1131L/V, I148T, I336K/T, I507del, I807M, IVS8T5, K1180T, L1065P, L967S, L997F, M1V, M470V, M952I, M952T, N1303K, P67L, Q1463Q, R1070Q, R1162X, R117C, R117H, R170H, R258G, R297Q, R31C, R352Q, R553X, R668C, R74W, R75Q, S1235R, S1255P, S485R, S977F, T338I, T854T, V201M, W1282X) were multiplexed into 6 wells; 14 SNPs (S492F, S945L, R74Q, R560T, R1162L, G85E, I1027T, R334W, R347P, G576A, 711+1G>T, 1001+11C>T, P1290P, 3199del6) were ascertained separately via TaqMan Gene Expression Assays, with repeat confirmation of all positive results. 3199del6 was genotyped via TaqMan on all samples that tested positive for I148T. In addition, the intron 8 boundary was directly sequenced in 873 subjects to determine the significance of the IVS8 T/TG tract.

LaRusch J., Jung J., General I.J., Lewis M.D., Park H.W., Brand R.E., Gelrud A., Anderson M.A., Banks P.A., Conwell D., Lawrence C., Romagnuolo J., Baillie J., Alkaade S., Cote G., Gardner T.B., Amann S.T., Slivka A., Sandhu B., Aloe A., Kienholz M.L., Yadav D., Barmada M.M., Bahar I., Lee M.G, & Whitcomb D.C. (2014). Mechanisms of CFTR Functional Variants That Impair Regulated Bicarbonate Permeation and Increase Risk for Pancreatitis but Not for Cystic Fibrosis. PLoS Genetics, 10(7), e1004376.

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Publication 2014

Acidic Pancreatic Trypsin Inhibitor Biological Assay Biopharmaceuticals CFTR protein, human Child Exons Gene Expression Genets Genome Gold Introns Iplex Mutation Pancreatitis Patients PRSS1 protein, human Single Nucleotide Polymorphism Vision

Gliquidone Modulates NLRP3 Inflammasome

The effects of gliquidone on LPS-mediated microglial and astroglial NLRP3 inflammasome activation and subsequent proinflammatory cytokine production were assessed in BV2 microglial cells and mouse primary astrocytes. In the preventive/pretreatment experiments, cells were first treated for 30 min with gliquidone (5 μM) or vehicle (1% DMSO) and then with LPS (200 ng/ml) or PBS for 5.5 h or 23.5 h. In the curative/post-treatment experiments, cells were first treated for 30 min with LPS (200 ng/ml) or PBS and then with gliquidone (5 μM) or vehicle (1% DMSO) for 5.5 h or 23.5 h. Total RNA was then extracted using TRIzol (Invitrogen, Waltham, MA, United States) as recommended by the manufacturer and reverse-transcribed (1 μg) to synthesize cDNA (GeNet Bio, Chungcheongnam-do, South Korea). The cDNA was used with Fast SYBR Green Master Mix (Thermo Fisher Scientific, Waltham, MA, United States) to perform real-time q-PCR in a QuantStudio 5 Real-Time PCR System (Applied Biosystems, Thermo Fisher Scientific, Waltham, MA, United States). The cycle threshold (Ct) values of the mRNA levels of factors related to inflammasome and inflammation were normalized to the Ct value for gapdh, and the fold change relative to the vehicle-treated control was quantified. Used primers are shown in Table 3.

Kim J., Park J.H., Shah K., Mitchell S.J., Cho K, & Hoe H.S. (2021). The Anti-diabetic Drug Gliquidone Modulates Lipopolysaccharide-Mediated Microglial Neuroinflammatory Responses by Inhibiting the NLRP3 Inflammasome. Frontiers in Aging Neuroscience, 13, 754123.

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Publication 2021

Astrocytes Cells Cytokine DNA, Complementary Fast Green GAPDH protein, human Genets gliquidone Inflammasomes Inflammation Microglia Mus Oligonucleotide Primers RNA, Messenger Sulfoxide, Dimethyl SYBR Green I trizol

Most recents protocols related to «Genets»

Restoring Mitochondrial Function in Friedreich's Ataxia Neurons

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Example 2

The next experiments asked whether inhibition of the same set of FXN-RFs would also upregulate transcription of the TRE-FXN gene in post-mitotic neurons, which is the cell type most relevant to FA. To derive post-mitotic FA neurons, FA(GM23404) iPSCs were stably transduced with lentiviral vectors over-expressing Neurogenin-1 and Neurogenin-2 to drive neuronal differentiation, according to published methods (Busskamp et al. 2014, Mol Syst Biol 10:760); for convenience, these cells are referred to herein as FA neurons. Neuronal differentiation was assessed and confirmed by staining with the neuronal marker TUJ1 (FIG. 2A). As expected, the FA neurons were post-mitotic as evidenced by the lack of the mitotic marker phosphorylated histone H3 (FIG. 2B). Treatment of FA neurons with an shRNA targeting any one of the 10 FXN-RFs upregulated TRE-FXN transcription (FIG. 2C) and increased frataxin (FIG. 2D) to levels comparable to that of normal neurons. Likewise, treatment of FA neurons with small molecule FXN-RF inhibitors also upregulated TRE-FXN transcription (FIG. 2E) and increased frataxin (FIG. 2F) to levels comparable to that of normal neurons.

It was next determined whether shRNA-mediated inhibition of FXN-RFs could ameliorate two of the characteristic mitochondrial defects of FA neurons: (1) increased levels of reactive oxygen species (ROS), and (2) decreased oxygen consumption. To assay for mitochondrial dysfunction, FA neurons an FXN-RF shRNA or treated with a small molecule FXN-RF inhibitor were stained with MitoSOX, (an indicator of mitochondrial superoxide levels, or ROS-generating mitochondria) followed by FACS analysis. FIG. 3A shows that FA neurons expressing an NS shRNA accumulated increased mitochondrial ROS production compared to EZH2- or HDAC5-knockdown FA neurons. FIG. 3B shows that FA neurons had increased levels of mitochondrial ROS production compared to normal neurons (Codazzi et al., (2016) Hum Mol Genet 25(22): 4847-485). Notably, inhibition of FXN-RFs in FA neurons restored mitochondrial ROS production to levels comparable to that observed in normal neurons. In the second set of experiments, mitochondrial oxygen consumption, which is related to ATP production, was measured using an Agilent Seahorse XF Analyzer (Divakaruni et al., (2014) Methods Enzymol 547:309-54). FIG. 3C shows that oxygen consumption in FA neurons was ˜60% of the level observed in normal neurons. Notably, inhibition of FXN-RFs in FA neurons restored oxygen consumption to levels comparable to that observed in normal neurons. Collectively, these preliminary results provide important proof-of-concept that inhibition of FXN-RFs can ameliorate the mitochondrial defects of FA post-mitotic neurons.

Mitochondrial dysfunction results in reduced levels of several mitochondrial Fe-S proteins, such as aconitase 2 (ACO2), iron-sulfur cluster assembly enzyme (ISCU) and NADH:ubiquinone oxidoreductase core subunit S3 (NDUFS3), and lipoic acid-containing proteins, such as pyruvate dehydrogenase (PDH) and 2-oxoglutarate dehydrogenase (OGDH), as well as elevated levels of mitochondria superoxide dismutase (SOD2) (Urrutia et al., (2014) Front Pharmacol 5:38). Immunoblot analysis is performed using methods known in the art to determine whether treatment with an FXN-RF shRNA or a small molecule FXN-RF inhibitor restores the normal levels of these mitochondrial proteins in FA neurons.

US11873494B2. Genetic and pharmacological transcriptional upregulation of the repressed FXN gene as a therapeutic strategy for Friedreich ataxia (2024-01-16). University of Massachusetts [US]. Inventors: Michael R. Green [US], Minggang Fang [US].

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Patent 2024

Aconitate Hydratase Biological Assay Cells Cloning Vectors Enzymes EZH2 protein, human frataxin Genets HDAC5 protein, human Histone H3 Immunoblotting Induced Pluripotent Stem Cells inhibitors Iron Ketoglutarate Dehydrogenase Complex Mitochondria Mitochondrial Inheritance Mitochondrial Proteins MitoSOX NADH NADH Dehydrogenase Complex 1 NEUROG1 protein, human Neurons Oxidoreductase Oxygen Consumption Proteins Protein Subunits Psychological Inhibition Pyruvates Reactive Oxygen Species Repression, Psychology Seahorses Short Hairpin RNA Sulfur sulofenur Superoxide Dismutase Superoxides Thioctic Acid Transcription, Genetic

Comprehensive Profiling of NSCLC Immunotherapy Cohorts

The training cohort included 344 patients with NSCLC treated with ICIs (Immunotherapy, MSKCC, Nat Genet 2019) (9 (link)). Validation cohort 1 was integrated from three public cohorts sequenced by the WES, including 56 patients with NSCLC treated with anti-PD-(L)1 therapy (15 (link)), 75 patients with NSCLC treated with PD-1 plus CTLA-4 blockade (LUAD only) (16 (link)), and 69 patients with NSCLC treated with anti-PD-(L)1 monotherapy at Sun Yat-sen University Cancer Center (SYSUCC) (17 (link)). Validation cohort 2 was a pan-cancer cohort including 1181 patients treated with anti-PD-(L)1 therapy (From Samstein cohort, 350 patients with NSCLC, and 130 patients with unknown cancer were excluded) (9 (link)). Validation cohort 3 was also a pan-cancer cohort including 193 patients treated with anti-PD-(L)1 therapy (15 (link)) (From Miao cohort, 56 patients with NSCLC were excluded). Both training and validation cohorts were selected based on the following criteria: (i) patients with no mutation information were excluded; (ii) synonymous mutation, copy number variation, and fusion genes were excluded; (iii) genes were mutated in at least three samples. In addition, data from non-ICI treatment TCGA NSCLC cohorts were used for further exploration, including RNA-seq data downloaded from UCSC Xena (University of California Santa Cruz) (https://xenabrowser.net/datapages/), immune subtype data along with survival data acquired from Thorsson et al. (18 (link)), and mutation data obtained from Ellrott et al. (29 (link)). In addition, six single-cell RNA sequencing data of LUAD patients from Bischoff, P., et al. (30 (link)) were included to reveal the gene expression features in different cell types (30 (link)). In addition, a retrospective southwest hospital clinical (SHC) cohort, with 82 lung cancer patients, was utilized to analyze the correlation between the predictive model and TMB. Of these, 77 were NSCLC, and the remaining were primary lung cancer. Survival data could not be acquired because of the loss of follow-up after surgery. All the samples were collected in the Southwest Hospital, and multiple gene panel target sequencing was conducted. The detailed clinical characteristics of patients in the training cohort, validation cohort 1-3, TCGA cohort, and SHC cohort are summarized in Supplementary Tables 2–7. The detailed mutations data of SHC cohort are listed in Supplementary Table 8.

He P., Liu J., Xu Q., Ma H., Niu B., Huang G, & Wu W. (2023). Development and validation of a mutation-based model to predict immunotherapeutic efficacy in NSCLC. Frontiers in Oncology, 13, 1089179.

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Publication 2023

Cells Copy Number Polymorphism Cytotoxic T-Lymphocyte Antigen 4 Gene Expression Genes Genets Immunotherapy Lung Cancer Malignant Neoplasms Multiple Birth Offspring Mutation Neoplasms, Unknown Primary Non-Small Cell Lung Carcinoma Operative Surgical Procedures Patients RNA-Seq Silent Mutation

Informal Settlements in Peri-Urban Woldia, Ethiopia

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Publication 2023

Earthquakes Face Genets Globalization Imprinting (Psychology) Landslides Obstetric Delivery Urban Population

Clonal Analysis across Species Range

We identified clonal individuals from two circular plots and from 12 populations sampled across the species range using the package poppr version 2.9.3 [50 (link)]. The clonal analysis for ANF4 was divided among three sampling sites because of its broad spatial distribution. We set the threshold to distinguish unique genotypes from clones by generating frequency histograms of genetic distance between samples and identifying the location of a large gap between values (Figure S2). We then constructed UPGMA trees and identified clones as any group of individuals that diverged to the right of the distance value corresponding to this threshold (Figure S2). For the circular plots, we filtered individuals missing more than 25% of SNPs from the plot in SJSBP2 and individuals missing >45% of SNPs from one plot in ANF4, as this method was sensitive to missing data. Because over half of the individuals in our second circular plot located in ANF4 were missing significant amounts of data, we excluded this plot from analysis. Our distance threshold to distinguish individuals was set at 0.03 for both plots. For population clonal analysis, we constrained the ploidy level to diploid before running the analysis in poppr. We identified the number of clones (genets) and the number of individuals per clone (ramets) in each population or site. Clonal richness (CR) was calculated as the number of genotypes (G) relative to the number (N) of samples assessed (CR = (G-1)/(N-1); [51 (link)]). The spatial arrangement of the samples assigned to the corresponding clones was visualized using their longitude and latitude coordinates; however, given the classification status of this species, we cannot report the specific coordinates.

Hanko G.R., Vogel M.T., Negrón-Ortiz V, & Moore R.C. (2023). High Prevalence of Clonal Reproduction and Low Genetic Diversity in Scutellaria floridana, a Federally Threatened Florida-Endemic Mint. Plants, 12(4), 919.

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Publication 2023

Clone Cells Diploidy Genets Genotype Single Nucleotide Polymorphism Trees

Population Genomic Analysis of Clonal Samples

The variants in the final quality-filtered variant call format (VCF) file were pruned for LD using Plink (80 (link)) independent pairwise function (settings: 50, 10, 0.1), providing an independent and essentially random set of SNPs. The resulting variants were further filtered to SNPs with 0% missingness to a set of 27,854 genome-wide SNPs. We conducted PCA in R (Version 4.2.0; R core team 2022) using the packages adegenet and vcfr (81 (link)–83 (no links found)). We also conducted discriminant analysis of principal components (DAPC); a flexible population assignment method also implemented in adegenet (84 (link)). Using the cross-validation procedure outlined in the vignette, we found that retention of 10 PCs performed best in population assignment. We performed population assignment tests for each clone retaining 10 PCs and two discriminant functions and plotted the results as a bar plot to visualize the probabilities of population assignment. DAPC is flexible enough to handle mixed clonal and temporal sampling—two factors that violate other assignment techniques [e.g., STRUCTURE (85 (link))]. From the PCA and the DAPC, results, we determined that the samples could be assigned to three “subpopulations” according to the depth of their recovery (Results). Using these three subpopulations as designations, we estimated overall site-wise F_st using the basic.stats function and mean pairwise genetic distance genet.dist function in the R package hierfstat (86 ). In addition, we estimated nucleotide diversity (π) in 10-kb windows throughout the genome for each of the temporal subpopulations using the program PIXY (87 ).

Wersebe M.J, & Weider L.J. (2023). Resurrection genomics provides molecular and phenotypic evidence of rapid adaptation to salinization in a keystone aquatic species. Proceedings of the National Academy of Sciences of the United States of America, 120(6), e2217276120.

Publication 2023

Clone Cells Genets Genome Nucleotides Operator, Genetic Population Group Retention (Psychology) Single Nucleotide Polymorphism

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What are the different types or variations of genets?

Genets are a diverse group of small carnivores, with several distinct species and subspecies found across Africa and Asia. The common genet, the largest and most widespread species, can be found throughout much of sub-Saharan Africa. Other notable genet species include the Central African oyan, the Aquatic genet, and the Abyssinian genet, each with their own unique physical characteristics and habitat preferences. Researchers may need to consider these different genet varieties when designing studies or selecting samples for their work.

What are some common challenges or considerations when working with genets?

Genets can be difficult to study due to their elusive, nocturnal, and arboreal nature. Accessing and observing genet populations in their natural habitats can present logistical challenges. Additionally, genets are sensitive to disturbances, which can impact their natural behaviors and skew research findings. Researchers must take care to minimize stress and disturbance when capturing, handling, or studying genets in order to obtain the most accurate and reproducible data.

How can PubCompare.ai assist researchers working with genets?

PubCompare.ai's AI-powered platfrom can help researchers optimize their work with genets in several ways. First, the platfrom can efficiently screen the literature to identify the most reproducible and accurate protocols related to genet research, saving time and effort. Second, the platfrom's AI analysis can pinpoint critical insights, such as key differences in the effectiveness of various genet-related protocols, enabling researchers to choose the best approach for their specific needs. This can help ensure high-quality, reliable results from genet studies. Overall, PubCompare.ai takes the guesswork out of genet research, allowing scientists to focus on their work with greater confidence and efficiency.

More about "Genets"

Genets are a fascinating group of small, nocturnal carnivores found in Africa and Asia.
These agile, tree-climbing mammals are known for their distinctive spotted coats, which can be black-and-white or gray-and-white.
Belonging to the Viverridae family, genets are closely related to mongooses and are skilled hunters, preying on small rodents, birds, insects, and even fruit.
Researchers studying these unique creatures may be interested in exploring their remarkable adaptations, behaviors, and ecological roles.
For example, genets use their sharp claws and prehensile tails to navigate the treetops with ease, highlighting their remarkable arboreal abilities.
When it comes to research tools and techniques, scientists may leverage a variety of products to support their work on genets.
For instance, Prime Taq Premix or TRIzol reagent could be used for DNA extraction and PCR amplification, while M-MLV reverse transcriptase or SuPrimeScript RT Premix might be employed for RNA-based analyses.
Genomic DNA isolation kits and Taq DNA polymerase could also prove valuable in genet-related studies.
By utilizing the insights and resources available, researchers can optimize their investigations into these fascinating carnivores, unlocking a deeper understanding of their biology, ecology, and evolution.
PubCompare.ai's AI-powered platform can further assist in this process, helping researchers identify the most reproducible and accurate research protocols related to genets, taking the guesswork out of their work.